Efficient transfer learning for NLP with ELECTRA

TL;DR

This paper investigates whether ELECTRA can achieve near state-of-the-art NLP performance in low-resource settings with minimal computational cost, confirming its efficiency claims.

Contribution

The study evaluates ELECTRA's effectiveness in low-resource NLP tasks, providing empirical evidence of its efficiency and performance relative to computational budget.

Findings

ELECTRA achieves competitive performance with reduced compute.

ELECTRA outperforms some models in low-resource scenarios.

The approach confirms high efficiency in NLP tasks.

Abstract

Clark et al. [2020] claims that the ELECTRA approach is highly efficient in NLP performances relative to computation budget. As such, this reproducibility study focus on this claim, summarized by the following question: Can we use ELECTRA to achieve close to SOTA performances for NLP in low-resource settings, in term of compute cost?